



import collections
from typing import List, Optional, Union

from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert import BertTokenizer


a      :  Optional[int]			     =			logging.get_logger(__name__)

a      :  List[Any]			     =			{'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}

a      :  int			     =			{
    'vocab_file': {
        'facebook/dpr-ctx_encoder-single-nq-base': (
            'https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/vocab.txt'
        ),
        'facebook/dpr-ctx_encoder-multiset-base': (
            'https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/vocab.txt'
        ),
    },
    'tokenizer_file': {
        'facebook/dpr-ctx_encoder-single-nq-base': (
            'https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/tokenizer.json'
        ),
        'facebook/dpr-ctx_encoder-multiset-base': (
            'https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/tokenizer.json'
        ),
    },
}
a      :  List[Any]			     =			{
    'vocab_file': {
        'facebook/dpr-question_encoder-single-nq-base': (
            'https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/vocab.txt'
        ),
        'facebook/dpr-question_encoder-multiset-base': (
            'https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/vocab.txt'
        ),
    },
    'tokenizer_file': {
        'facebook/dpr-question_encoder-single-nq-base': (
            'https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/tokenizer.json'
        ),
        'facebook/dpr-question_encoder-multiset-base': (
            'https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/tokenizer.json'
        ),
    },
}
a      :  Dict			     =			{
    'vocab_file': {
        'facebook/dpr-reader-single-nq-base': (
            'https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/vocab.txt'
        ),
        'facebook/dpr-reader-multiset-base': (
            'https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/vocab.txt'
        ),
    },
    'tokenizer_file': {
        'facebook/dpr-reader-single-nq-base': (
            'https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/tokenizer.json'
        ),
        'facebook/dpr-reader-multiset-base': (
            'https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/tokenizer.json'
        ),
    },
}

a      :  List[str]			     =			{
    'facebook/dpr-ctx_encoder-single-nq-base': 512,
    'facebook/dpr-ctx_encoder-multiset-base': 512,
}
a      :  Optional[Any]			     =			{
    'facebook/dpr-question_encoder-single-nq-base': 512,
    'facebook/dpr-question_encoder-multiset-base': 512,
}
a      :  Dict			     =			{
    'facebook/dpr-reader-single-nq-base': 512,
    'facebook/dpr-reader-multiset-base': 512,
}


a      :  Any			     =			{
    'facebook/dpr-ctx_encoder-single-nq-base': {'do_lower_case': True},
    'facebook/dpr-ctx_encoder-multiset-base': {'do_lower_case': True},
}
a      :  Optional[Any]			     =			{
    'facebook/dpr-question_encoder-single-nq-base': {'do_lower_case': True},
    'facebook/dpr-question_encoder-multiset-base': {'do_lower_case': True},
}
a      :  str			     =			{
    'facebook/dpr-reader-single-nq-base': {'do_lower_case': True},
    'facebook/dpr-reader-multiset-base': {'do_lower_case': True},
}







class 				_a				(   _lowerCAmelCase					):
			A      		=	VOCAB_FILES_NAMES
			A      		=	CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
			A      		=	CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			A      		=	CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION







class 				_a				(   _lowerCAmelCase					):
			A      		=	VOCAB_FILES_NAMES
			A      		=	QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
			A      		=	QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			A      		=	QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION






a      :  Union[str, Any]			     =			collections.namedtuple(
    'DPRSpanPrediction', ['span_score', 'relevance_score', 'doc_id', 'start_index', 'end_index', 'text']
)

a      :  Tuple			     =			collections.namedtuple('DPRReaderOutput', ['start_logits', 'end_logits', 'relevance_logits'])


a      :  List[str]			     =			r'\n    Return a dictionary with the token ids of the input strings and other information to give to `.decode_best_spans`.\n    It converts the strings of a question and different passages (title and text) in a sequence of IDs (integers),\n    using the tokenizer and vocabulary. The resulting `input_ids` is a matrix of size `(n_passages, sequence_length)`\n    with the format:\n\n    ```\n    [CLS] <question token ids> [SEP] <titles ids> [SEP] <texts ids>\n    ```\n\n    Args:\n        questions (`str` or `List[str]`):\n            The questions to be encoded. You can specify one question for many passages. In this case, the question\n            will be duplicated like `[questions] * n_passages`. Otherwise you have to specify as many questions as in\n            `titles` or `texts`.\n        titles (`str` or `List[str]`):\n            The passages titles to be encoded. This can be a string or a list of strings if there are several passages.\n        texts (`str` or `List[str]`):\n            The passages texts to be encoded. This can be a string or a list of strings if there are several passages.\n        padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`):\n            Activates and controls padding. Accepts the following values:\n\n            - `True` or `\'longest\'`: Pad to the longest sequence in the batch (or no padding if only a single sequence\n              if provided).\n            - `\'max_length\'`: Pad to a maximum length specified with the argument `max_length` or to the maximum\n              acceptable input length for the model if that argument is not provided.\n            - `False` or `\'do_not_pad\'` (default): No padding (i.e., can output a batch with sequences of different\n              lengths).\n        truncation (`bool`, `str` or [`~tokenization_utils_base.TruncationStrategy`], *optional*, defaults to `False`):\n            Activates and controls truncation. Accepts the following values:\n\n            - `True` or `\'longest_first\'`: Truncate to a maximum length specified with the argument `max_length` or to\n              the maximum acceptable input length for the model if that argument is not provided. This will truncate\n              token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a batch\n              of pairs) is provided.\n            - `\'only_first\'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum\n              acceptable input length for the model if that argument is not provided. This will only truncate the first\n              sequence of a pair if a pair of sequences (or a batch of pairs) is provided.\n            - `\'only_second\'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum\n              acceptable input length for the model if that argument is not provided. This will only truncate the\n              second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.\n            - `False` or `\'do_not_truncate\'` (default): No truncation (i.e., can output batch with sequence lengths\n              greater than the model maximum admissible input size).\n        max_length (`int`, *optional*):\n                Controls the maximum length to use by one of the truncation/padding parameters.\n\n                If left unset or set to `None`, this will use the predefined model maximum length if a maximum length\n                is required by one of the truncation/padding parameters. If the model has no specific maximum input\n                length (like XLNet) truncation/padding to a maximum length will be deactivated.\n        return_tensors (`str` or [`~utils.TensorType`], *optional*):\n                If set, will return tensors instead of list of python integers. Acceptable values are:\n\n                - `\'tf\'`: Return TensorFlow `tf.constant` objects.\n                - `\'pt\'`: Return PyTorch `torch.Tensor` objects.\n                - `\'np\'`: Return Numpy `np.ndarray` objects.\n        return_attention_mask (`bool`, *optional*):\n            Whether or not to return the attention mask. If not set, will return the attention mask according to the\n            specific tokenizer\'s default, defined by the `return_outputs` attribute.\n\n            [What are attention masks?](../glossary#attention-mask)\n\n    Returns:\n        `Dict[str, List[List[int]]]`: A dictionary with the following keys:\n\n        - `input_ids`: List of token ids to be fed to a model.\n        - `attention_mask`: List of indices specifying which tokens should be attended to by the model.\n    '







@add_start_docstrings(_lowerCAmelCase					)
class 				_a				:

			def __call__(self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_ = None,   SCREAMING_SNAKE_CASE_ = None,   SCREAMING_SNAKE_CASE_ = False,   SCREAMING_SNAKE_CASE_ = False,   SCREAMING_SNAKE_CASE_ = None,   SCREAMING_SNAKE_CASE_ = None,   SCREAMING_SNAKE_CASE_ = None,   **SCREAMING_SNAKE_CASE_,   )       ->   BatchEncoding:
						if titles is None and texts is None:
									return super().__call__(
									    SCREAMING_SNAKE_CASE_,   padding=SCREAMING_SNAKE_CASE_,   truncation=SCREAMING_SNAKE_CASE_,   max_length=SCREAMING_SNAKE_CASE_,   return_tensors=SCREAMING_SNAKE_CASE_,   return_attention_mask=SCREAMING_SNAKE_CASE_,   **SCREAMING_SNAKE_CASE_,   )
						elif titles is None or texts is None:
									UpperCAmelCase_:		Union[str, Any]							      =     titles if texts is None else texts
									return super().__call__(
									    SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   padding=SCREAMING_SNAKE_CASE_,   truncation=SCREAMING_SNAKE_CASE_,   max_length=SCREAMING_SNAKE_CASE_,   return_tensors=SCREAMING_SNAKE_CASE_,   return_attention_mask=SCREAMING_SNAKE_CASE_,   **SCREAMING_SNAKE_CASE_,   )
						UpperCAmelCase_:		Tuple							      =     titles if not isinstance(SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					) else [titles]
						UpperCAmelCase_:		List[str]							      =     texts if not isinstance(SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					) else [texts]
						UpperCAmelCase_:		List[str]							      =     len(SCREAMING_SNAKE_CASE_					)
						UpperCAmelCase_:		Union[str, Any]							      =     questions if not isinstance(SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					) else [questions] * n_passages
						if len(SCREAMING_SNAKE_CASE_					) != len(SCREAMING_SNAKE_CASE_					):
									raise ValueError(
									    f'There should be as many titles than texts but got {len(SCREAMING_SNAKE_CASE_					)} titles and {len(SCREAMING_SNAKE_CASE_					)} texts.'					)
						UpperCAmelCase_:		List[Any]							      =     super().__call__(SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   padding=SCREAMING_SNAKE_CASE_,   truncation=SCREAMING_SNAKE_CASE_					)["""input_ids"""]
						UpperCAmelCase_:		List[str]							      =     super().__call__(SCREAMING_SNAKE_CASE_,   add_special_tokens=SCREAMING_SNAKE_CASE_,   padding=SCREAMING_SNAKE_CASE_,   truncation=SCREAMING_SNAKE_CASE_					)["""input_ids"""]
						UpperCAmelCase_:		Tuple							      =     {
						    """input_ids""": [
						        (encoded_question_and_title + encoded_text)[:max_length]
						        if max_length is not None and truncation
						        else encoded_question_and_title + encoded_text
						        for encoded_question_and_title, encoded_text in zip(SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					)
						    ]
						}
						if return_attention_mask is not False:
									UpperCAmelCase_:		str							      =     []
									for input_ids in encoded_inputs["input_ids"]:
												attention_mask.append([int(input_id != self.pad_token_id					) for input_id in input_ids]					)
									UpperCAmelCase_:		str							      =     attention_mask
						return self.pad(SCREAMING_SNAKE_CASE_,   padding=SCREAMING_SNAKE_CASE_,   max_length=SCREAMING_SNAKE_CASE_,   return_tensors=SCREAMING_SNAKE_CASE_					)

			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_ = 16,   SCREAMING_SNAKE_CASE_ = 64,   SCREAMING_SNAKE_CASE_ = 4,   )       ->   List[DPRSpanPrediction]:
						UpperCAmelCase_:		Dict							      =     reader_input["""input_ids"""]
						UpperCAmelCase_      ,							UpperCAmelCase_      ,							UpperCAmelCase_:		Optional[int]							      =     reader_output[:3]
						UpperCAmelCase_:		str							      =     len(SCREAMING_SNAKE_CASE_					)
						UpperCAmelCase_:		str							      =     sorted(range(SCREAMING_SNAKE_CASE_					),   reverse=SCREAMING_SNAKE_CASE_,   key=relevance_logits.__getitem__					)
						UpperCAmelCase_:		List[DPRReaderOutput]							      =     []
						for doc_id in sorted_docs:
									UpperCAmelCase_:		Optional[Any]							      =     list(input_ids[doc_id]					)
									# assuming question & title information is at the beginning of the sequence
									UpperCAmelCase_:		Optional[int]							      =     sequence_ids.index(self.sep_token_id,   2					) + 1  # second sep id
									if sequence_ids[-1] == self.pad_token_id:
												UpperCAmelCase_:		Any							      =     sequence_ids.index(self.pad_token_id					)
									else:
												UpperCAmelCase_:		Optional[Any]							      =     len(SCREAMING_SNAKE_CASE_					)

									UpperCAmelCase_:		Tuple							      =     self._get_best_spans(
									    start_logits=start_logits[doc_id][passage_offset:sequence_len],   end_logits=end_logits[doc_id][passage_offset:sequence_len],   max_answer_length=SCREAMING_SNAKE_CASE_,   top_spans=SCREAMING_SNAKE_CASE_,   )
									for start_index, end_index in best_spans:
												start_index += passage_offset
												end_index += passage_offset
												nbest_spans_predictions.append(
												    DPRSpanPrediction(
												        span_score=start_logits[doc_id][start_index] + end_logits[doc_id][end_index],   relevance_score=relevance_logits[doc_id],   doc_id=SCREAMING_SNAKE_CASE_,   start_index=SCREAMING_SNAKE_CASE_,   end_index=SCREAMING_SNAKE_CASE_,   text=self.decode(sequence_ids[start_index : end_index + 1]					),   )					)
									if len(SCREAMING_SNAKE_CASE_					) >= num_spans:
												break
						return nbest_spans_predictions[:num_spans]

			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   )       ->   List[DPRSpanPrediction]:
						UpperCAmelCase_:		Any							      =     []
						for start_index, start_score in enumerate(SCREAMING_SNAKE_CASE_					):
									for answer_length, end_score in enumerate(end_logits[start_index : start_index + max_answer_length]					):
												scores.append(((start_index, start_index + answer_length), start_score + end_score)					)
						UpperCAmelCase_:		Optional[Any]							      =     sorted(SCREAMING_SNAKE_CASE_,   key=lambda SCREAMING_SNAKE_CASE_					: x[1],   reverse=SCREAMING_SNAKE_CASE_					)
						UpperCAmelCase_:		str							      =     []
						for (start_index, end_index), score in scores:
									if start_index > end_index:
												raise ValueError(f'Wrong span indices: [{start_index}:{end_index}]'					)
									UpperCAmelCase_:		int							      =     end_index - start_index + 1
									if length > max_answer_length:
												raise ValueError(f'Span is too long: {length} > {max_answer_length}'					)
									if any(
									    start_index <= prev_start_index <= prev_end_index <= end_index
									    or prev_start_index <= start_index <= end_index <= prev_end_index
									    for (prev_start_index, prev_end_index) in chosen_span_intervals					):
												continue
									chosen_span_intervals.append((start_index, end_index)					)

									if len(SCREAMING_SNAKE_CASE_					) == top_spans:
												break
						return chosen_span_intervals







@add_end_docstrings(_lowerCAmelCase					)
class 				_a				(   _lowerCAmelCase ,		_lowerCAmelCase					):
			A      		=	VOCAB_FILES_NAMES
			A      		=	READER_PRETRAINED_VOCAB_FILES_MAP
			A      		=	READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			A      		=	READER_PRETRAINED_INIT_CONFIGURATION
			A      		=	['''input_ids''', '''attention_mask''']











#
# This a `torch.distributed` diagnostics script that checks that all GPUs in the cluster (one or
# many nodes) can talk to each other via nccl and allocate gpu memory.
#
# To run first adjust the number of processes and nodes:
#
# python -m torch.distributed.run --nproc_per_node 2 --nnodes 1 torch-distributed-gpu-test.py
#
# You may need to add --master_addr $MASTER_ADDR --master_port $MASTER_PORT if using a custom addr:port
#
# You can also use the rdzv API: --rdzv_endpoint $MASTER_ADDR:$MASTER_PORT --rdzv_backend c10d
#
# use torch.distributed.launch instead of torch.distributed.run for torch < 1.9
#
# If you get a hanging in `barrier` calls you have some network issues, you may try to debug this with:
#
# NCCL_DEBUG=INFO python -m torch.distributed.run --nproc_per_node 2 --nnodes 1 torch-distributed-gpu-test.py
#
# which should tell you what's going on behind the scenes.
#
#
# This script can be run via `srun` in the SLURM environment as well. Here is a SLURM script that
# runs on 2 nodes of 4 gpus per node:
#
# #SBATCH --job-name=test-nodes        # name
# #SBATCH --nodes=2                    # nodes
# #SBATCH --ntasks-per-node=1          # crucial - only 1 task per dist per node!
# #SBATCH --cpus-per-task=10           # number of cores per tasks
# #SBATCH --gres=gpu:4                 # number of gpus
# #SBATCH --time 0:05:00               # maximum execution time (HH:MM:SS)
# #SBATCH --output=%x-%j.out           # output file name
#
# GPUS_PER_NODE=4
# MASTER_ADDR=$(scontrol show hostnames $SLURM_JOB_NODELIST | head -n 1)
# MASTER_PORT=6000
#
# srun --jobid $SLURM_JOBID bash -c 'python -m torch.distributed.run \
# --nproc_per_node $GPUS_PER_NODE --nnodes $SLURM_NNODES --node_rank $SLURM_PROCID \
# --master_addr $MASTER_ADDR --master_port $MASTER_PORT \
# torch-distributed-gpu-test.py'
#

import fcntl
import os
import socket

import torch
import torch.distributed as dist







def  lowerCAmelCase_	(*lowerCAmelCase__:		Dict  ):





			"""simple docstring"""


			with open(lowerCAmelCase__		,						"""r"""  ) as fh:
						fcntl.flock(lowerCAmelCase__		,						fcntl.LOCK_EX  )
						try:
									print(*lowerCAmelCase__  )
						finally:
									fcntl.flock(lowerCAmelCase__		,						fcntl.LOCK_UN  )


a      :  Tuple			     =			int(os.environ['LOCAL_RANK'])
torch.cuda.set_device(local_rank)
a      :  Union[str, Any]			     =			torch.device('cuda', local_rank)
a      :  Any			     =			socket.gethostname()

a      :  Optional[Any]			     =			F'''[{hostname}-{local_rank}]'''

try:
	# test distributed
	dist.init_process_group('nccl')
	dist.all_reduce(torch.ones(1).to(device), op=dist.ReduceOp.SUM)
	dist.barrier()

	# test cuda is available and can allocate memory
	torch.cuda.is_available()
	torch.ones(1).cuda(local_rank)

	# global rank
	a      :  Optional[Any]			     =			dist.get_rank()
	a      :  List[Any]			     =			dist.get_world_size()

	printflock(F'''{gpu} is OK (global rank: {rank}/{world_size})''')

	dist.barrier()
	if rank == 0:
		printflock(F'''pt={torch.__version__}, cuda={torch.version.cuda}, nccl={torch.cuda.nccl.version()}''')

except Exception:
	printflock(F'''{gpu} is broken''')
	raise






